The quantum evolutionary algorithm is based on the quantum superposition state and the quantum computing. Compared to the traditional evolutionary algorithm, QEA has great global search ability; however, its complexity limits the operational ability and the efficiency, at the same time, its local search ability is relatively poor. In this paper, the author presents an improved hybrid intelligen...

Quantum computers are expected to accelerate solving combinatorial optimization problems, including algorithms such as Grover adaptive search and quantum approximate algorithm (QAOA). However, many problems involve constraints which, when imposed soft in the cost function, can negatively impact performance of algorithm. In this paper, we propose fermionic (FQAOA) for with constraints. Specifica...

Quantum phase estimation (QPE) is the key subroutine of several quantum computing algorithms as well a central ingredient in computational chemistry and simulation. While QPE strategies have focused on single phase, applications to simultaneous phases may bring substantial advantages; for instance, presence spatial or temporal constraints. In this work, we study Bayesian algorithm parallel (sim...